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Computational Neuroethology: A Call to Action
Open accessAbstract
The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.
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“Neuroscience studies in particular have embraced novel tools for motion tracking, pose estimation (Fig. 1d, e), and behavioral classification (e.g., ref. 141).”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [141]
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Cites
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,506
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0209-y · Cited by 5,187
On aims and methods of ethology
Tinbergen, N. (1963) · Zeitschrift für Tierpsychologie · doi:10.1111/j.1439-0310.1963.tb01161.x · Cited by 4,551
(1961) · Information and Control · doi:10.1016/s0019-9958(61)80064-8 · Cited by 2,904
Rowan, W., & Tinbergen, N. (1952) · Bird-Banding · doi:10.2307/4510368 · Cited by 2,706
Hutt, S. J. (1977) · Animal Behaviour · doi:10.1016/0003-3472(77)90136-1 · Cited by 2,327
Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178
Neural population dynamics during reaching
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012) · Nature · doi:10.1038/nature11129 · Cited by 1,704
Vehicles: Experiments in Synthetic Psychology.
Dennett, D. C., & Braitenberg, V. (1986) · The Philosophical Review · doi:10.2307/2185146 · Cited by 1,624
Research works printed but not linked: 5
- Graving, 2019, Fast and robust animal pose estimation, bioRxiv
- Liu, 2018, Temporal processing and context dependency in C. elegans mechanosensation, arXiv
- Musall, 2018, Movement-related activity dominates cortex during sensory-guided decision making, bioRxiv
- Scholz, 2018, Predicting natural behavior from whole-brain neural dynamics, bioRxiv
- Sutton, 1998, Vol. 2
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Big behavior: challenges and opportunities in a new era of deep behavior profiling
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Huang, K., Han, Y., Chen, K., Pan, H., Zhao, G., Yi, W., Li, X., Liu, S., Wei, P., & Wang, L. (2021) · Nature Communications · doi:10.1038/s41467-021-22970-y · Cited by 128
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Rage Against the Machine: Advancing the study of aggression ethology via machine learning.
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Vogt, C. C., Zipple, M. N., Sprockett, D. D., Miller, C. H., Hardy, S. X., Arthur, M. K., Greenstein, A. M., Colvin, M. S., Michel, L. M., Moeller, A. H., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-01809-0 · Cited by 44
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Brann, D. H., & Datta, S. R. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102119-103452 · Cited by 37
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FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in Drosophila
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Automated maternal behavior during early life in rodents (AMBER) pipeline
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Analysis of behavioral flow resolves latent phenotypes
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Self-citation
Metadata
- Authors
- Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A.
- Affiliations
- Harvard University (US)
- California Institute of Technology (US)
- Howard Hughes Medical Institute (US)
- Princeton University (US)
- Corresponding author
- Datta, Sandeep Robert
- Leifer, Andrew
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 104
- Issue
- 1
- Pages
- 11-24
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC6981239
- OpenAlex
- W2980030724
- Cited by
- 453
- References
- 148
- Citations by year
- 2026: 61 · 2025: 71 · 2024: 73 · 2023: 66 · 2022: 68 · 2021: 65 · 2020: 46 · 2019: 3
- FWCI
- 5.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- Simons Foundation
- National Institutes of Health (U24NS109520, U19NS112953, RO1MH112593, U19NS108179, RO1DC016222)
- National Science Foundation (1845137, PHY-1734030)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Education › Undergraduate Neuroscience Education and Research
- Keywords
- naturalistic behavior, behavior quantification, neural recording, neural manipulation, systems neuroscience
- MeSH terms
- Machine Learning, Animals, Behavior, Animal, Brain, Ethology, Neurosciences, Electrophysiological Phenomena
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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